Methods and apparatus to generate an augmented environment including a weight indicator for a vehicle

ABSTRACT

Methods, apparatus, systems, and articles of manufacture are disclosed to generate an augmented environment including a weight indicator for a vehicle. An example disclosed apparatus includes a sensor interface to receive load data associated with a vehicle, and receive live video data from a camera, the live video data including a location of an object in the vehicle, a load mapper to generate a map of loads on the vehicle based on the load data, an object-to-weight correlator to correlate a load of the map of loads with the object, and an augmented reality generator to generate an augmented environment identifying the location of the object and the load correlated with the object.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 16/191,134, filed on Nov. 14, 2018 and entitled “METHODS ANDAPPARATUS TO GENERATE AN AUGMENTED ENVIRONMENT INCLUDING A WEIGHTINDICATOR FOR A VEHICLE,” which claims the benefit of U.S. ProvisionalPatent Application Ser. No. 62/497,317, which was filed on Oct. 15,2018. U.S. patent application Ser. No. 16/191,134 and U.S. ProvisionalPatent Application Ser. No. 62/497,317 are incorporated herein in theirentirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to vehicle loads and, moreparticularly, to methods and apparatus to generate an augmentedenvironment including a weight indicator for a vehicle.

BACKGROUND

All vehicles have a maximum limit on a load the front and rear axles canwithstand. In some examples, each axle has a gross axle weight rating(GAWR) that corresponds to the maximum load that may be supported by theaxle. Additionally, weight can be poorly distributed on/in the vehicle.If an axle of the vehicle is overloaded or the vehicle is unbalanced,handling degradation, brake problems, and poor headlight aim can occur.In some examples, a vehicle may be misloaded if a particular axle orsuspension assembly is bearing a disproportionate amount of the totalload on the vehicle. Loading issues can often be relieved byredistributing objects (e.g., cargo, passengers, etc.) to differentsections of the vehicle.

Mobile devices (e.g., smart phones, headsets, etc.) can now supportaugmented reality (AR) technology that allows virtual information toaugment live video data captured by the mobile device. Augmented realitytechnology can add and/or remove information from the video data as thevideo data is presented to user (e.g., by the display of the mobiledevice). In some examples, AR technology can allow information to beintuitively presented to a user by overlaying relevant virtualinformation onto video of a physical environment in real-time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an environment of an example vehicle load managerthat can be used with an example vehicle with which the examplesdisclosed herein can be implemented.

FIG. 2 is a block diagram depicting the vehicle load condition managerof FIG. 1.

FIG. 3 is an example illustration of a vehicle and an example augmentedreality environment generated by the vehicle load manager 102 of FIG. 1.

FIG. 4 is another example illustration of a vehicle and an exampleaugmented reality environment generated by the vehicle load manager 102of FIG. 1.

FIG. 5 is a flowchart representative of machine readable instructionsthat may be executed to implement the vehicle load condition manager ofFIG. 1.

FIG. 6 is a block diagram of an example processing platform structuredto execute the instructions of FIG. 5 to implement the vehicle loadcondition manager of FIG. 1.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Misloading a vehicle can degrade the reliability of the vehicle. As usedherein, the phrase “misloading a vehicle” and all variations thereof,refers to distributing objects on/in a vehicle in such a manner thatadversely affects the performance of the vehicle, and can, for example,include exceeding the GAWR of one or both axles, exceeding a weightrating of a suspension assembly, unbalancing a weight distributionassociated with the vehicle, etc. In some examples, redistributing theload (passengers, cargo, etc.) on a vehicle can alleviate some or allproblems caused by misloading a vehicle. In other examples, removing aload from the vehicle can be required. Traditional means of displayingthis information to a user (e.g., a warning light of the dashboard,etc.) may not be intuitive or provide sufficient information for a userto quickly and effectively understand and then correct a loading issue.This lack of intuitiveness or information may lead to a misloadedvehicle.

Methods and apparatus disclosed herein combine load data collected byvehicle sensors and live video data to generate an augmented realityenvironment including the loading condition of a vehicle and weightsborne by components of the vehicle. As used herein, the phrase“augmented reality environment” (also referred to herein as an“augmented environment”) is a virtual environment that includes arepresentation of a physical space (e.g., captured by a video camera) onwhich computer generated perceptual information is overlaid (e.g.,virtual objects are added, physical objects are hidden, etc.). In someexamples disclosed herein, objects on/in the vehicle are identified andcorrelated to load data detected by vehicle sensor(s). In some examplesdisclosed herein, a map of object shapes, positions, and loads isgenerated. In some examples disclosed herein, guidance in the form ofvisual instructions are displayed in the augmented reality environmentto indicate how objects can be positioned to properly load the vehicle.

In some examples disclosed herein, a mobile device (e.g., a smartphone,a headset, etc.) with a camera can be used to scan a vehicle todetermine what objects are on/in the vehicle. In this example, themobile device can detect a visual anchor on the vehicle to determine theposition of identified objects relative to the visual anchor. As usedherein, a visual anchor is a visually identifiable feature at a fixedlocation on a vehicle that can be used to reference the locations ofobjects in/on the vehicle. In other examples disclosed herein, a cameraintegral with the vehicle (e.g., a camera mounted above a bed of atruck) can be used to identify an object loaded in a specific area ofthe vehicle (e.g., a truck bed). In some examples, machine visiontechniques can be used to identify objects. In some examples disclosedherein, the augmented reality environment can be displayed on a displayintegral with the vehicle. In other examples disclosed herein, theaugmented reality environment can be presented on a display of themobile device.

FIG. 1 illustrates an environment 100 of an example vehicle load manager102 that can be used with an example vehicle 104 with which the examplesdisclosed herein can be implemented. The vehicle 104 includes one ormore example wheel and suspension assemblies 105, one or more exampleweight sensor(s) 106, an example trailer hitch 109, an example trailerweight sensor 110, and an example camera 122. In some examples, thevehicle load manager 102 can output information to an example display114 and/or output information to an example mobile device 120 via anexample network 118. In the illustrated example, the vehicle 104 is aconsumer automobile. In other examples, the vehicle 104 can be acommercial truck, a motorcycle, a motorized cart, an all-terrainvehicle, a bus, a motorized scooter, a locomotive, or any other vehicle.

The example vehicle load manager 102 enables the generation of anaugmented reality environment to guide a user to properly load thevehicle 104. For example, the vehicle load manager 102 can receiveinformation from sensors (e.g., the weight sensor(s) 106, the trailerweight sensor 110, etc.), process the data, and output an augmentedreality environment (e.g., to the display 114 or the mobile device 120).In some examples, the vehicle load manager 102 can additionally receivelive video data from a camera of the mobile device 120 and/or theexample camera 122. In some examples, the vehicle load manager 102 canfurther generate guidance to be presented to the user to instruct theuser how to redistribute the load on the vehicle 104. The example camera122 can be, for example, mounted in a center high mounted stop light(CHMSL) of the vehicle (e.g., the brake light indicator above the rearwindow of a truck bed, etc.).

In some examples, one or more of the wheel and suspension assemblies 105can be coupled via an axle (e.g., a front axle, a rear axle, etc.).Additionally, one or more of the wheel and suspension assemblies 105 caninclude a weight sensor 106 (e.g., an axle load sensor). In someexamples, the weight sensors 106 are ride height sensors that measurethe compression of specific ones of the wheel and suspension assemblies105 (e.g., a deflection of an elastic element of the wheel andsuspension assembly 105), from which load information can be derived. Inother examples, the weight sensors 106 can be transducers capable ofconverting load information into an electrical signal to be received bythe vehicle load manager 102.

In the illustrated example, the vehicle 104 can tow a trailer coupled tothe vehicle 104 via the trailer hitch 109. A trailer may exert a load onthe vehicle 104, which can be measured by the example trailer weightsensor 110. In some examples, the trailer weight sensor 110 can beintegrated into the trailer hitch 109. In some examples, the trailerweight sensor 110 is a force sensor (e.g., a magnetoelastic sensor, aload cell, a strain gauge, an accelerometer, etc.) capable of measuringforces and/or moments at the trailer hitch 109. In some examples, thetrailer weight sensor 110 measures the load corresponding to the one ormore loads exerted on the vehicle 104 by a towed trailer (e.g., totalload of the trailer, tongue, etc.).

In some examples, the display 114 can present a user of the vehicle 104with an augmented reality environment produced by the vehicle loadmanager 102. In these examples, the display 114 can display an augmentedreality environment including one or more instructions, load conditionsof the vehicle 104, and/or weight indications (e.g., how much load isapplied to an axle or the wheel and suspension assembly 105).

In some examples, the vehicle load manager 102 is connected to thenetwork 118. For example, the network 118 can be a WiFi network or aBlueTooth® network. In other examples, the network 118 can beimplemented by any suitable wired and/or wireless network(s) including,for example, one or more data buses, one or more Local Area Networks(LANs), one or more wireless LANs, one or more cellular networks, one ormore public networks, etc. The example network 118 enables the examplevehicle load manager 102 to be in communication with devices external tothe vehicle 104 (e.g., the mobile device 120). As used herein, thephrase “in communication,” including variances thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components and does not require direct physical (e.g.,wired) communication and/or constant communication but, rather, includesselective communication at periodic or aperiodic intervals, as well asone-time events.

In the illustrated example of FIG. 1, the example mobile device 120includes a camera and a user interface (e.g., a display). The examplemobile device 120 can be one of or a combination of a smart phone, atablet, a smart watch, a VR/AR headset, smart glasses, etc. In theillustrated example, the mobile device 120 communicates with the vehicleload manager 102 via the network 118. In other examples, the mobiledevice 120 can be connected to the vehicle load manager 102 via a wiredconnection.

FIG. 2 is a block diagram depicting the vehicle load manager 102 ofFIG. 1. The example vehicle load manager 102 includes an example sensorinterface 202, an example load mapper 204, an example object identifier206, an example object-to-weight correlator 208, an example conditiondeterminer 210, an example guidance generator 212 and an exampleaugmented reality generator 214. The vehicle load manager 102 can beimplemented fully on the vehicle 104, fully on the mobile device 120 orany combination thereof.

The example sensor interface 202 receives sensor data from the sensorsof the example vehicle 104. For example, the sensor interface 202 canreceive input from one or more of the example weight sensors 106 of FIG.1, the example trailer weight sensor 110 of FIG. 1, and/or any othersensors (e.g., a fuel level sensor, an engine speed sensor, a vehiclespeed sensor, etc.). In some examples, the sensor interface 202 canreceive live video data from the mobile device 120. In some examples,the sensor interface 202 distributes received sensor data to at leastone of the load mapper 204, the object identifier 206, and/or theaugmented reality generator 214. For example, the sensor interface 202can distribute load data (e.g., data received from the weight sensors106) to the load mapper 204.

The example load mapper 204 determines a map of the loads on the vehicle104. For example, the load mapper 204 can analyze the sensor datadistributed by the sensor interface 202 to determine the location andweight of objects on/in the vehicle 104. For example, the load mapper204 can analyze the sensor data to determine that an object weighing 85pounds is placed on the passenger seat of the vehicle 104. In someexamples, the load mapper 204 can generate a visual representation ofthe vehicle 104 with the additional loads on the vehicle 104.

The example object identifier 206 reviews the data distributed by thesensor interface 202 to determine the location of objects loading thevehicle 104. For example, the object identifier 206 can analyze livevideo data from the mobile device 120 and/or the camera 122 to visuallyidentify an object on/in the vehicle 104. In some examples, the objectidentifier 206 can identify a visual anchor to create a reference pointon the vehicle 104 to reference the location of the identified objects.In other examples, if the camera 122 is fixed to the vehicle 104, theobject identifier 206 can compare the live video data to an image of thevehicle 104 without objects to identify objects in the live video data.In some examples, the object identifier 206 can use machine learningalgorithms to identify and locate visual objects. In some examples, theobject identifier 206 can use machine vision techniques (e.g., patternrecognition, edge detection, color detection, keypoint mapping, imagehistogram, etc.).

The example object-to-weight correlator 208 correlates the load mapgenerated by the load mapper 204 to the objects identified by the objectidentifier 206. For example, the object-to-weight correlator 208 canassociate a load in the bed of a vehicle 104 with an object identifiedby the object identifier 206 in the same location (e.g., tag theidentified object with the corresponding load, etc.). In some examples,the object-to-weight correlator 208 can generate a map of shapes, loads,and positions of the object(s) on/in the vehicle 104 based on the loadmap and identified objects.

The example condition determiner 210 analyzes the load map generated bythe load mapper 204 and/or sensor data for the sensor interface 202 todetermine the load condition of the vehicle 104. For example, thecondition determiner 210 can determine if the load map indicates thatthe vehicle 104 is overloaded. In other examples, the conditiondeterminer 210 can determine if a GAWR of the vehicle 104 has beenexceeded. In other examples, the condition determiner 210 can determinethat vehicle 104 is not misloaded. In some examples, the conditiondeterminer 210 can determine whether rearranging the objects on/in thevehicle 104 would alleviate an adverse load condition(s) of the vehicle104.

The example guidance generator 212 generates instructions toredistribute loads on the vehicle 104 to improve the load condition ofthe vehicle 104. For example, the guidance generator 212 can determinethat an object in the bed of the vehicle 104 should be moved to adifferent location in the bed to better distribute the load on thevehicle 104. In some examples, the guidance generator 212 can generatean instruction that indicates the location and/or direction the objectshould be moved to correct the loading condition. In some examples, theguidance generator 212 can generate an instruction to guide the user toremove objects on/in the vehicle 104. In other examples, if the vehicle104 is properly loaded (e.g., not misloaded), the guidance generator 212does not generate instructions. In some such examples, the guidancegenerator 212 can generate an indication that the vehicle 104 isproperly loaded. In some examples, the guidance generator 212 cangenerate instructions even if the vehicle 104 is properly loaded.

The example augmented reality generator 214 generates an augmentedreality environment based on the data received by the sensor interface202, the object-to-weight correlator 208, and the guidance generator212. The example augmented reality generator 214 generates an augmentedreality environment to be presented via the display 114 and/or themobile device 120. The augmented reality generator 214 can, for example,create a visual indication of the load on each of the wheel andsuspension assemblies 105 and/or axles of the vehicle 104. In someexamples, the augmented reality generator 214 can generate a warning ifthe vehicle 104 is misloaded. In some examples, the augmented realitygenerator 214 can present a guidance instruction based on the input fromthe guidance generator 212 (e.g., instructions 314 of FIG. 3, theinstructions 410 of FIG. 4, etc.). In some examples, the augmentedreality generator 214 can update the augmented reality environment inreal-time as the objects in the vehicle 104 are moved by a user. Inother examples, the augmented reality generator 214 can update thegenerated augmented reality environment periodically at a predeterminedinterval or in response to a request from a user.

While an example manner of implementing the vehicle load manager 102 ofFIG. 1 is illustrated in FIG. 2, one or more of the elements, processes,and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated, and/or implemented in any other way.Further, the example sensor interface 202, the example load mapper 204,the example object identifier 206, the example object-to-weightcorrelator 208, the example condition determiner 210, the exampleguidance generator 212, the example augmented reality generator 214,and/or, more generally, the example vehicle load manager 102 of FIG. 3may be implemented by hardware, software, firmware, and/or anycombination of hardware, software, and/or firmware. Thus, for example,any of the example sensor interface 202, the example load mapper 204,the example object identifier 206, the example object-to-weightcorrelator 208, the example condition determiner 210, the exampleguidance generator 212, the example augmented reality generator 214,and/or, more generally, the example vehicle load manager 102 could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), programmable controller(s), graphicsprocessing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)), and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example, sensor interface 202, the example load mapper 204, theexample object identifier 206, the example object-to-weight correlator208, the example condition determiner 210, the example guidancegenerator 212, the example augmented reality generator 214 is/are herebyexpressly defined to include a non-transitory computer readable storagedevice or storage disk such as a memory, a digital versatile disk (DVD),a compact disk (CD), a Blu-ray disk, etc., including the software and/orfirmware. Further still, the example vehicle load manager 102 of FIG. 1may include one or more elements, processes, and/or devices in additionto, or instead of, those illustrated in FIG. 2, and/or may include morethan one of any or all of the illustrated elements, processes, anddevices. As used herein, the phrase “in communication,” includingvariations thereof, encompasses direct communication and/or indirectcommunication through one or more intermediary components, and does notrequire direct physical (e.g., wired) communication and/or constantcommunication, but rather additionally includes selective communicationat periodic intervals, scheduled intervals, aperiodic intervals, and/orone-time events.

FIG. 3 is an example illustration 300 of the vehicle 104 including andan example augmented reality environment generated by the vehicle loadmanager 102. The example illustration 300 includes the example vehicle104 of FIG. 1, an example object 302, and an example visual anchor 304.The example illustration 300 further includes the example mobile device120 with an example display 306 displaying an example augmented realityenvironment 308 generated by the vehicle load manager 102. The exampleaugmented reality environment 308 includes an example warning 310, anexample front axle weight indicator 312A, an example rear axle weightindicator 312B and an example instruction 314. While an example of thegraphical user interface of the augmented reality environment 308 isillustrated in FIG. 3, any other suitable graphical user interface maybe used to represent the augmented reality environment 308 and/or theoutput of the vehicle load manager 102.

The example vehicle 104 is loaded by the object 302. In the illustratedexample, the object 302 is loaded in the bed of the vehicle 104. Inother examples, the object 302 may be on/in any other location of thevehicle 104. In the illustrated example, the load associated with theobject 302 exceeds the GAWR of the rear axle of the vehicle 104, whichcauses the vehicle 104 to be misloaded. In some examples, the vehicleload manager 102 detects the location, shape, and load associated withthe example object 302. In some examples, a camera associated with themobile device 120 and/or the camera 122 scans the object 302 such thatthe vehicle load manager 102 can identify the object 302.

In the illustrated example, a user of the mobile device 120 scans thevisual anchor 304 with the mobile device 120 (e.g., captures the visualanchor 304 in the video data generated by the mobile device 120) toallow the physical location(s) of the object 302 to be determined by thevehicle load manager 102. In the illustrated example, the visual anchor304 is a handle of a front driver door of the vehicle 104. In otherexamples, the visual anchor 304 may be any other visually identifiablefeature of the vehicle 104 (e.g., a hubcap, the fuel door, etc.). Insome examples, the visual anchor 304 may be a sticker and/or othervisual feature placed on the vehicle 104 by a user. In some examples, ifthe visual anchor 304 is not detected by the mobile device 120, theaugmented reality environment 308 can include an instruction to the userto continue scanning the vehicle 104 until the visual anchor 304 isidentified by the vehicle load manager 102. In the illustrated example,the vehicle 104 includes only the visual anchor 304. In other examples,the vehicle 104 can include any number of anchors in addition to thevisual anchor 304.

In the illustrated example, the augmented reality environment 308 isgenerated based on live video data captured by a camera of the mobiledevice 120 with the output of the vehicle load manager 102. That is, asthe live video data is presented via the display of the mobile device120, the live video data is being augmented by the vehicle load manager102. In some examples, the augmented reality environment 308 is updatedin real time based on the video data captured by the mobile device 120and changes to the load condition of the vehicle 104 (e.g., caused by auser adjusting the position of the object 302, etc.).

In the illustrated example of FIG. 3, the warning 310 includes the textthe “rear axle overloaded,” indicating a GAWR of the rear axle has beenexceeded. In some examples, the warning 310 can illustrate the output ofthe condition determiner 210. In other examples, the warning 310 mayrepresent any other potentially adverse loading condition(s) of thevehicle 104 (e.g., the front axle is overload, the load is unbalanced,etc.). In other examples where there is no adverse loading condition onthe vehicle 104, the warning 310 may be absent. In this example, theaugmented reality environment 308 may further include an indication thatthe vehicle 104 is properly loaded.

In the illustrated example of FIG. 3, the front axle weight indicator312A is a rectangle underneath the front axle of the vehicle 104 in theaugmented reality environment 308 and indicates the front axle is loadedwith 2,955 lbs. Similarly, in the illustrated example, the rear axleweight indicator 312B is a rectangle underneath the rear axle of thevehicle 104 in the augmented reality environment 308 and indicates therear axle is loaded with 4,630 lbs. In other examples, the front axleweight indicators 312A and the rear axle weight indicator 312B can beplaced in any suitable location in the augmented reality environment 308to indicate the load on the front and/or rear axles. In other examples,the front axle weight indicator 312A and/or the rear axle weightindicator 312B may include an audio notification to the user. In someexamples, each wheel and suspension assembly 105 can have individualweight indicators (e.g., an indicator for the forward driver wheel andsuspension assembly 105, an indicator for the forward passenger wheeland suspension assembly 105, etc.).

In the illustrated example, the instruction 314 includes the text “moveload forward” and an arrow pointing to the front of the vehicle 104. Inother examples, the instruction 314 can be in any other suitablelocation to indicate that the object 302 should be moved forwardrelative to the vehicle 104. In some examples, the instruction 314 caninclude a specific distance and direction to move the object 302. Insome examples, the instruction 314 does not include text. In someexamples, the instruction 314 may include any other visualrepresentation to indicate how the load on the vehicle 104 should beredistributed (e.g., a line, a visual representation of the object 302in the correct location, etc.). In some examples, the instruction 314may include a non-visual notification to the user (e.g., an audionotification, a vibration, etc.).

FIG. 4 is another example illustration 400 of the vehicle 104 and anexample augmented reality environment 402 generated by the vehicle loadmanager 102 of FIG. 1. In the illustrated example, the augmented realityenvironment 402 is displayed via the display 114 of FIG. 1 and isgenerated based on the output the vehicle load manager 102 and livevideo data gathered by the camera 122 of FIG. 1. That is, as the livevideo data is presented via the display of the display 114, the livevideo data is being augmented by vehicle load manager 102. The examplevehicle 104 further includes an example bed 403 holding an example firstobject 406A and an example second object 406B. The augmented realityenvironment 402 includes an example first weight indication 408A, anexample second weight indication 408B, an example warning 404, andexample instructions 410. In some examples, the augmented realityenvironment 402 is updated in real time based on the live video datacaptured by the camera 122 and changes in the load condition of the bed403 (e.g., caused by a user adjusting the positions of the first object406A and/or the second object 406B, etc.).

In the illustrated example, the first object 406A is a portable coolerand the second object 406B is a traffic cone. In other examples, thefirst object 406A and the second object 406B can be any other objects.In some examples, the vehicle load manager 102 of FIG. 1 can determinethe load, shape, and position associated with both the first object 406Aand the second object 406B. In the illustrated example, the vehicle loadmanager 102 determines that the vehicle 104 is unbalanced and that thefirst object 406A should be moved to properly balance the vehicle 104.

In some examples, the warning 404 can display the output of thecondition determiner 210 of FIG. 2. In the illustrated example of FIG.4, the warning 404 includes the text “load adjustment recommended,”which indicates the vehicle 104 is unbalanced. In other examples, thewarning 404 can indicate any other potentially adverse loadingcondition(s) of the vehicle 104 (e.g., the front axle is overloaded,etc.). In other examples where there is no adverse loading condition onthe vehicle 104, the warning 404 may not be present in the augmentedreality environment 402. In this example, the augmented realityenvironment 402 can further display indication that the vehicle 104 isproperly loaded.

In the illustrated example, the instructions 410 is an arrow pointing tothe right with respect to the display 114 including the text “move 6”indicating the first object 406A is to be moved 6 inches to the right onthe vehicle 104 to properly balance the vehicle 104. In other examples,the instructions 410 can be in any suitable location and can include anysuitable text and/or visual representation (e.g., a line, a visualrepresentation of the first object 406A in the correct location, etc.).In some examples, the instructions 410 can include a non-visualnotification to the user (e.g., an audio notification, a vibration,etc.). In some examples, the instructions 410 can include multiple steps(e.g., moving both the first object 406A and the second object 406B).

A flowchart representative of example methods, hardware implementedstate machines, and/or any combination thereof for implementing thevehicle load manager 102 of FIG. 2 is shown in FIG. 5. The method can beimplemented using machine readable instructions that may be anexecutable program or portion of an executable program for execution bya computer processor such as the processor 612 shown in the exampleprocessor platform 600 discussed below in connection with FIG. 6. Theprogram may be embodied in software stored on a non-transitory computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, aDVD, a Blu-ray disk, or a memory associated with the processor 612, butthe entire program and/or parts thereof could alternatively be executedby a device other than the processor 612 and/or embodied in firmware ordedicated hardware. Further, although the example program is describedwith reference to the flowchart illustrated in FIG. 5, many othermethods of implementing the example vehicle load manager 102 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks may be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc4.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example method of FIG. 5 may be implementedusing executable instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory, and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C.

The method 500 of FIG. 5 begins at block 502. At block 502, if thevehicle load manager 102 is enabled, the method 500 advances to block504. If the augmented vehicle load manager 102 is not enabled, themethod 500 ends. For example, the vehicle load manager 102 can beenabled by a user. In other examples, the vehicle load manager 102 canautomatically become enabled if the vehicle 104 is misloaded.

At block 504, the sensor interface 202 receives load data. For example,the sensor interface 202 can interface with one or more of the weightsensor(s) 106 associated with the wheel and suspension assemblies 105 ofthe vehicle 104. In some examples, the sensor interface 202 can furtherreceive load data from the trailer weight sensor 110 and/or any otherload sensors of the vehicles (e.g., load sensors associated with theseats of the vehicle 104). In some examples, the sensor interface 202can convert the received load data into a format (e.g., a digitalsignal, a bit-based value, etc.) processable by the vehicle load manager102. In some examples, the sensor interface 202 can distribute thereceived load data to any other elements of the vehicle load manager 102(e.g., load mapper 204, the object identifier 206, etc.).

At block 506, the sensor interface 202 receives auxiliary sensor dataand video data. For example, the sensor interface 202 can receive datafrom any other sensors on the vehicle 104 necessary to generate a loadmap of the vehicle (e.g., a fuel level sensor, etc.). In some examples,the sensor interface 202 can convert the received load(s) into a formatprocessable by the vehicle load manager 102. In some examples, thesensor interface 202 can receive live video data generated by the mobiledevice 120 and/or the camera 122. In some examples, the sensor interface202 can distribute the received auxiliary data and/or live video data toany other components of the vehicle load manager 102 (e.g., load mapper204, the object identifier 206, etc.). In some examples, the live videodata captures a visual anchor (e.g., the visual anchor 304 of FIG. 3)and/or one or more objects in/on the vehicle 104.

At block 508, the load mapper 204 generates a load map of the vehicle104. For example, the load mapper 204 can analyze the load datadistributed by the sensor interface 202 to generate a map of loads onthe vehicle 104. In some examples, the load mapper 204 can generate avisual representation of the loads on the vehicle 104. At block 510, ifthe object identifier 206 identifies an anchor (e.g., the visual anchor304 of FIG. 3) on the vehicle 104 captured in the live video data, themethod 500 advances to block 514. If an anchor is not identified by theobject identifier 206, the method 500 advances to block 512.

At block 512, the object identifier 206 alerts the user to scan ananchor of the vehicle 104. For example, the object identifier 206 cangenerate an alert to be displayed (e.g., on a display of the mobiledevice 120, the display 114, etc.). In some examples, the objectidentifier 206 can augment the live video data to include an indicationto scan a visual anchor on the live video data. In some examples, theobject identifier 206 can issue a non-visual alert to the user (e.g.,vibrating the mobile device, an audible message, etc.). For example, theobject identifier 206 may alert the user to reposition the cameragenerating the live video data to better capture the visual anchor 304.

At block 514, the object identifier 206 identifies objects in the livevideo data. For example, the object identifier 206 can process the livevideo data received by the sensor interface 202 to identify objectson/in the vehicle 104. In some examples, the object identifier 206 canidentify the locations of identified objects relative to the visualanchor 304.

At block 516, the object-to-weight correlator 208 correlates thedetected objects with the load map. For example, the object-to-weightcorrelator 208 can associated identified objects (e.g., identified bythe object identifier 206) with the load map (e.g., generated by theload mapper 204) in a nearby position. In some examples, theobject-to-weight correlator 208 generates a visual map of the load,shape, and position of objects on/in the vehicle 104.

At block 518, the condition determiner 210 determines if loadingguidance is required. For example, the condition determiner 210 candetermine if the vehicle 104 is misloaded. In some examples, conditiondeterminer 210 can determine if the vehicle 104 is not optimally loaded.In some examples, the condition determiner 210 can transmit thedetermined condition to the augmented reality generator 214. If thecondition determiner 210 determines that loading guidance is required,the method 500 advances to block 520. If the condition determiner 210determines that loading guidance is not needed, the method 500 advancesto block 520.

At block 520, the guidance generator 212 generates loading guidance. Forexample, the guidance generator 212 can determine that the objects inand/or on the vehicle 104 should be rearranged to correctly load thevehicle 104. In some examples, the guidance generator 212 can determinethat objects should be removed from the vehicle 104. In some examples,the guidance generator 212 can indicate the location and distance aspecific object in/on the vehicle 104 should be moved to alleviateadverse loading conditions. Additionally or alternatively, the guidancegenerator 212 can generate a visual representation (e.g., an arrowincluding text) indicating how one or more objects should be rearrangedon the vehicle 104.

At block 522, the augmented reality generator 214 generates an augmentedreality environment. For example, the augmented reality generator 214can combine the visual map generated by the object-to-weight correlator208 with the live video data (e.g., presented on the mobile device 120and/or the camera 122). In some examples, the augmented realitygenerator 214 can generate weight indicators to identify the weight ofobjects on/in the vehicle 104 (e.g., the weight indicators 408A and 408Bof FIG. 4). In some examples, the augmented reality generator 214 cangenerate an indication of a load carried by the front axle or rear axleof the vehicle 104 (e.g., the weight indicators 312A and 312B). In someexamples, the augmented reality generator 214 can generate an indicationof the load carried by each of the wheel and suspension assemblies 105of FIG. 1.

At block 524, the condition determiner 210 determines if additionalloading guidance is required. For example, the condition determiner 210can evaluate a new map generated by the object-to-weight correlator 208to determine if the vehicle 104 is misloaded. In other examples, thecondition determiner can process the live video data to determine if auser has followed the guidance generated by the guidance generator 212.If the loading condition has been resolved, the method 500 ends. Ifadditional loading guidance is required, the method 500 returns to block522 to generate new loading guidance.

FIG. 6 is a block diagram of an example processor platform 600 capableof executing instructions of FIG. 5 to implement the vehicle loadmanager 102 of FIG. 2. The processor platform 600 can be, for example, aserver, a personal computer, a workstation, a self-learning machine(e.g., a neural network), a mobile device (e.g., a cell phone, a smartphone, a tablet such as an iPad™), a personal digital assistant (PDA),an Internet appliance, a DVD player, a CD player, a digital videorecorder, a Blu-ray player, a gaming console, a personal video recorder,a headset or other wearable device, or any other type of computingdevice.

The processor platform 600 of the illustrated example includes aprocessor 612. The processor 612 of the illustrated example is hardware.For example, the processor 612 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor 612 implements the example sensor interface202, the example load mapper 204, the example object identifier 206, theexample object-to-weight correlator 208, the example conditiondeterminer 210, the example guidance generator 212 and the exampleaugmented reality generator 214.

The processor 612 of the illustrated example includes a local memory 613(e.g., a cache). The processor 612 of the illustrated example is incommunication with a main memory including a volatile memory 614 and anon-volatile memory 616 via a bus 618. The volatile memory 614 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®), and/or any other type of random access memory device. Thenon-volatile memory 616 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 614, 616is controlled by a memory controller.

The processor platform 600 of the illustrated example also includes aninterface circuit 620. The interface circuit 620 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 622 are connectedto the interface circuit 620. The input device(s) 622 permit(s) a userto enter data and/or commands into the processor 612. The inputdevice(s) 622 can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, an isopoint device, and/or avoice recognition system.

One or more output devices 624 are also connected to the interfacecircuit 620 of the illustrated example. The output devices 624 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printer,and/or speaker. The interface circuit 620 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chip,and/or a graphics driver processor.

The interface circuit 620 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 626. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 600 of the illustrated example also includes oneor more mass storage devices 628 for storing software and/or data.Examples of such mass storage devices 628 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 632 to implement the methods of FIG.5 may be stored in the mass storage device 628, in the volatile memory614, in the non-volatile memory 616, and/or on a removablenon-transitory computer readable storage medium such as a CD or DVD.

Example 1 includes an apparatus, comprising a sensor interface toreceive load data associated with a vehicle, and receive live video datafrom a camera, the live video data including a location of an object inthe vehicle, a load mapper to generate a map of loads on the vehiclebased on the load data, an object-to-weight correlator to correlate aload of the map of loads with the object, and an augmented realitygenerator to generate an augmented environment identifying the locationof the object and the load correlated with the object.

Example 2 includes the apparatus of example 1, wherein the augmentedenvironment is updated in real-time.

Example 3 includes the apparatus of example 2, wherein the augmentedenvironment is presented to a user via a mobile device, the mobiledevice including the camera.

Example 4 includes the apparatus of example 2, wherein the augmentedenvironment is presented to a user via a display integrated in thevehicle.

Example 5 includes the apparatus of example 1, wherein theobject-to-weight correlator is further to process the live video data toidentify a visual anchor on the vehicle indicating a known location onthe vehicle.

Example 6 includes the apparatus of example 1 further including acondition determiner to determine a load condition of the vehicle basedon the load data, and a guidance generator to modify the augmentedenvironment with a visual indication based on the load condition, thevisual indication including an instruction to move the object.

Example 7 includes a method, comprising generating a map of loads on avehicle based on load data associated with a sensor of the vehicle,correlating a load of the map of loads with an object identified usinglive video data received from a camera, and generating an augmentedenvironment identifying a location of the object and the load correlatedwith the object.

Example 8 includes the method of example 7, the method further includingdetermining a load condition of the vehicle based on the load data andmodifying the augmented environment with a visual indication based onthe load condition, the visual indication including an instruction tomove the object.

Example 9 includes the method of example 8, wherein the visualindication is continuously updated as the objected is moved.

Example 10 includes the method of example 8, wherein the visualindication is an arrow indicating a direction the object is to be moved.

Example 11 includes the method of example 7 further including presentingthe augmented environment to a user via a display.

Example 12 includes the method of example 11, wherein the display andthe camera are included in a mobile device.

Example 13 includes the method of example 7 further includingidentifying a visual anchor indicating a known point on the vehicle.

Example 14 includes a non-transitory computer readable medium comprisinginstructions, which when executed cause a processor to at least generatea map of loads on a vehicle based on load data associated with a sensorof the vehicle, correlate a load of the map of loads with an object, theobject identified from live video data received from a camera, andgenerate an augmented environment identifying a location of the objectand the load correlated with the object.

Example 15 includes the non-transitory computer readable medium ofexample 14, further including instructions which when executed cause theprocessor to determine a load condition of the vehicle based on the loaddata and modify the augmented environment with a visual indication basedon the load condition, the visual indication including an instruction tomove the object.

Example 16 includes the non-transitory computer readable medium ofexample 15, wherein the visual indication is continuously updated as theobjected is moved.

Example 17 includes the non-transitory computer readable medium ofexample 15, wherein the visual indication is an arrow indicating adirection the object is to be moved.

Example 18 includes the non-transitory computer readable medium ofexample 14, further including instructions which when executed cause aprocessor to present the augmented environment to a user via a display.

Example 19 includes the non-transitory computer readable medium ofexample 18, wherein the display and the camera are included in a mobiledevice.

Example 20 includes the non-transitory computer readable medium ofexample 14, further including instructions which when executed cause theprocessor to identify a visual anchor indicating a known point on thevehicle.

Although certain example methods, apparatus, and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus, and articles of manufacture fairly falling within the scopeof the claims of this patent.

What is claimed is:
 1. A mobile computing device, comprising: memory;computer readable instructions stored in the memory; a user interface; acamera to capture video data including a location of an object in avehicle; processor circuitry to execute the computer readableinstructions to at least: transmit the video data to a second interfaceassociated with the vehicle; and present, via the user interface, anaugmented environment identifying the location of the object and a loadcorrelated with the object, the load correlated with the object based ona map of loads generated from sensor data of the vehicle.
 2. The mobilecomputing device of claim 1, wherein the processor circuitry executesthe computer readable instructions to update the augmented environmentin real-time.
 3. The mobile computing device of claim 1, wherein themobile computing device includes at least one of a smartphone, a tablet,a smart watch, a headset, or smart glasses.
 4. The mobile computingdevice of claim 1, wherein the processor circuitry executes the computerreadable instructions to: determine if the video data includes a visualanchor on the vehicle indicating a known location on the vehicle; and inresponse to determining the video data does not include the visualanchor, alert a user of the mobile computing device to scan the visualanchor.
 5. The mobile computing device of claim 1, wherein the processorcircuitry executes the computer readable instructions to: receive, fromthe second interface, a first visual indication associated with theaugmented environment, the first visual indication including aninstruction to move the object; and modify the augmented environmentwith the first visual indication.
 6. The mobile computing device ofclaim 5, wherein the processor circuitry executes the computer readableinstructions to: transmit updated video data to the second interface,the updated video data captured after a user has moved the object inresponse to the first visual indication; and receive, from the secondinterface, a second visual indication, the second visual indicationincluding at least one of (1) an alert that the vehicle is not adverselyloaded, or (2) a second instruction to move the object.
 7. The mobilecomputing device of claim 1, wherein the video data is transmitted tothe second interface via a Local Area Network (LAN) connection.
 8. Anon-transitory computer readable medium comprising instructions, whichwhen executed cause a processor to at least: capture, via a camera of amobile computing device, video data including a location of an object ina vehicle; transmit the video data to a second interface associated withthe vehicle; and present, via a user interface of the mobile computingdevice, an augmented environment identifying the location of the objectand a load correlated with the object, the load correlated with theobject based on a map of loads generated from sensor data of thevehicle.
 9. The non-transitory computer readable medium of claim 8,wherein the instructions when executed cause a processor to update theaugmented environment in real-time.
 10. The non-transitory computerreadable medium of claim 8, wherein the mobile computing device includesat least one of a smartphone, a tablet, a smart watch, a headset, orsmart glasses.
 11. The non-transitory computer readable medium of claim8, wherein the instructions when executed cause a processor to:determine if the video data includes a visual anchor on the vehicleindicating a known location on the vehicle; and in response todetermining the video data does not include the visual anchor, alert auser of the mobile computing device to scan the visual anchor.
 12. Thenon-transitory computer readable medium of claim 8, wherein theinstructions, when executed, cause a processor to: receive, from thesecond interface, a first visual indication associated with theaugmented environment, the first visual indication including a firstinstruction to move the object; and modify the augmented environmentwith the first visual indication.
 13. The non-transitory computerreadable medium of claim 12, wherein the instructions, when executed,cause a processor to: transmit updated video data to the secondinterface, the updated video data captured after a user has moved theobject in response to the first visual indication; and receive, from thesecond interface, a second visual indication, the second visualindication including at least one of (1) an alert that the vehicle isnot adversely loaded, or (2) a second instruction to move the object.14. The non-transitory computer readable medium of claim 12, wherein thefirst visual indication is an arrow indicating a direction the object isto be moved.
 15. A method including: capturing, via a camera of a mobilecomputing device, video data including a location of an object in avehicle; transmitting the video data to a second interface associatedwith the vehicle; and presenting, via a user interface of the mobilecomputing device, an augmented environment identifying the location ofthe object and a load correlated with the object, the load correlatedwith the object based on a map of loads generated from sensor data ofthe vehicle.
 16. The method of claim 15, further including updating theaugmented environment in real-time.
 17. The method of claim 15, whereinthe mobile computing device includes at least one of a smartphone, atablet, a smart watch, a headset, or smart glasses.
 18. The method ofclaim 15, further including: determining if the video data includes avisual anchor on the vehicle indicating a known location on the vehicle;and in response to determining the video data does not include thevisual anchor, alerting a user of the mobile computing device to scanthe visual anchor.
 19. The method of claim 15, further including:receiving, from the second interface, a first visual indicationassociated with the augmented environment, the first visual indicationincluding an instruction to move the object; and modifying the augmentedenvironment with the first visual indication.
 20. The method of claim19, further including: transmitting updated video data to the secondinterface, the updated video data captured after a user has moved theobject in response to the first visual indication; and receiving, fromthe second interface, a second visual indication, the second visualindication including at least one of (1) an alert that the vehicle isnot adversely loaded, or (2) a second instruction to move the object.